The three main compartments of a cell are the nucleus, the cytoplasm and the membrane. The central nucleus is surrounded by the cytoplasm which is a gel-like fluid surrounded by the cell membrane.
In order to carry out DNA ploidy measurements, cells are processed to remove as far as possible the cytoplasm and membrane leaving only the nucleus. In practice, some cytoplasm remains. The processed cells may then be mounted on a slide for imaging.
DNA ploidy is a cytogenetic term for the number of single sets of chromosomes in a cell or organism. Diploid cells contain one pair of chromosomes, the normal state, and hence have a ploidy of 2. If the DNA duplicates without a subsequent cell division, the cell will contain two pairs of chromosomes and will be referred to as tetraploid.
Aneuploid cells contain a number of chromosomes that is not a multiple of the normal number, i.e. not a multiple of 2. In humans, aneuploid cells are abnormal and are a strong indication of malignancy.
Image cytometry may be used to measure the ploidy in human cells. The amount of DNA in a nucleus can be determined from the amount of light absorbed by the nuclei after a Feulgen stain is applied. The optical density, a measure of the transmittance of an optical element, may be measured on the Feulgen stained nuclei. DNA ploidy may be determined by calculating an integrated optical density, i.e. the sum of the optical density over a whole cell nucleus.
Image cytometry has the advantage of only measuring verifiable nuclei and hence results in limited or no cell debris being included in the analysis, which increases accuracy. In the past, human intervention has been used to identify cell nuclei. There is an increasing desire to automate this process, which increases the number of cell nuclei captured and measured, which in turn increases the ability to review the statistical significance of sub-populations. Increased automation also reduces the effect of subjective variations introduced by a human. Automation also allows for accurate grouping of cell types.
Challenges for automation of such a process include firstly locating, focussing and capturing data of cell nuclei. Secondly, there is a need to segment and calculate certain image characteristics as required for DNA ploidy classification. Thirdly, classification is required to identify intact nuclei and separate them from artefacts.
The greater the accuracy of such automatic methods and calculations the clearer aneuploid nuclei populations will be in comparison to normal diploid or tetraploid populations.